Abstract: In order to avoid being subject to falling into local optimum when particle swarm optimization algorithm solves some complicated problems，improve the diversity of the population.A new parallel particle swarm optimization algorithm based on cultural algorithm frame is proposed，which makes the particle swarm optimization bring into cultural algorithm frame.In the cultural algorithm frame，population space and belief space composed by particle swarm have their own parallel evolution process and affect with each other，improve the diversity of population and reduce the possibility of falling into local optima effectively.It is proven that the improved parallel particle swarm optimization based on cultural algorithm can be better to find the global optima on different benchmark optimization functions，and improve the global search capability.